A brief review of machine learning methods for RNA methylation sites prediction.

Abstract

Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on for years for its fundamental role in regulating the many aspects of RNA function. Thanks to the big data generated in sequencing, machine learning methods have been developed for efficiently identifying methylation sites. In this review, we comprehensively explore machine learning based approaches for predicting 10 types of methylation of RNA, which include m6A, m5C, m7G, 5hmC, m1A, m5U, m6Am, and so on. Firstly, we reviewed three main aspects of machine learning which are data, features and learning algorithms. Then, we summarized all the methods that have been used to predict the 10 types of methylation. Furthermore, the emergent methods which were designed to predict multiple types of methylation were also reviewed. Finally, we discussed the future perspectives for RNA methylation sites prediction.